As the amount of multidimensional remotely sensed data is growing tremendously, Earth scientists need more efficient ways to search and analyze such data. In particular, extracting image content is emerging as one of the most powerful tools to perform data mining. One of the most promising methods to extract image content is image classification, which provides a labeling of each pixel in the image. In this paper, we concentrate on neural network classifiers and we show how information obtained with a wavelet transform can be integrated to such a classifier. We apply a local spatial frequency analysis, a wavelet transform, to account for statistical texture information in Landsat/TM imagery. Statistical texture is extracted with a continuous edge-texture composite wavelet transform. We show how this approach relates to texture information computed from a co-occurrence matrix. The network is then trained with both the texture information and the additional pixel labels provided by the ground truth data. Theory and preliminary results are described in this paper. In future work, this pre-processing will automatically generate a texture index which will be fed into the artificial neural network, thus providing better discriminants of Landsat/TM imagery.